Generally, the human depth estimated from a single camera view is inherently ambiguous. This ambiguity will make it hard to learn an accurate regression model. To handle this issue, we exploit the capability of probability prediction to capture the tolerance of depth ambiguity by predicting a depth distribution instead of an absolute depth, which has also been used in the field of object detection [1]}. Here, we assume that the depth distribution of each person is independent and follows a Laplace distribution \(L({z}^{\text{reg}}_{\text{root}},\ \lambda )\) . The probability density function of a Laplace random variable \(Z \sim L({Z}^{\text{reg}}_{\text{root}},\ \lambda )\)  is:
\(p_{\text{reg}}(Z) = \frac{1}{2\lambda } e^{-\frac{| Z - {Z}^{\text{reg}}_{\text{root}} |}{\lambda }}\) 
